ENERGY-AWARE JOB SCHEDULING AND CONSOLIDATION APPROACHES FOR WORKFLOWS IN CLOUD

• Main Author: Khaleel, Mustafa Ibrahim
• Format: Others
• Published: OpenSIUC 2016
• Online Access: https://opensiuc.lib.siu.edu/dissertations/1165; https://opensiuc.lib.siu.edu/cgi/viewcontent.cgi?article=2169&context=dissertations

Cloud computing offers several types of on-demand and scalable access to software, computing resources, and storage services through web browsers based on pay-as-you- go model. In order to meet the growing demand of active users and reduce the skyrocketing cost of electricity for powering the data centers, cloud service providers are highly motivated to implement performance guaranteed and cost-effective job schedulers. Many researchers have been focusing on scheduling jobs with high performance, and their primary concern has been execution time considerations. As a result of this thinking, little attention was paid to energy consumption and energy costs. However, nowadays energy cost has gained more and more attention from the service providers. This new reality has posed many new challenges for providers who are both concerned about meeting the execution time constraints and reducing energy costs. In recent years, there has been a growing body of research which focused on improving resource utilization by adopting new strategies and ideas that can be used to improve energy efficiency while maintaining high system throughput. One of these strategies used is known as task consolidation. This is one of the most effective techniques for increasing system-wide resource utilization. The research clearly shows that by switching o idle servers to sleep mode a vast amount of energy can be saved. In this research, a job scheduling approach called multi-procedure energy-aware heuristic scientific workflow scheduling method referred to as Time and Energy Aware Scheduling (TEAS) is proposed to tackle an energy optimization problem. This method is based on a rigorous cost and energy model that could be used to maximize resource utilization performance. The objectives focused on maximizing resource utilization and minimizing power consumption without compromising Quality of Service (QoS) such as workflow response time specified in the Service Level Agreements (SLA). The scientific applications are formulated as Directed Acycle Graph (DAG)-structured workflow to be processed as a group using virtualization techniques over cloud resources. Furthermore, the underlying cloud hardware/Virtual Machine (VM) resource availability is time-dependent because of the dual operation modes of on-demand and reservation. The resource provision and allocation algorithm can be separated into three steps with different objectives. The first step (Datacenter Selection) selects the most efficient data center to execute module applications. The second step (Time and Energy Aware Scheduling Forward Mapping) primarily focuses on estimating the execution time of scheduling a batch of workflows over VMs on underlying cloud servers and the objective is to achieve the minimum End-to-End Delay (EED). The last, and the most important step is related to the energy saving and resource utilization (Time and Energy Aware Scheduling Backward Mapping) which is concerned with minimizing energy consumption. This task is accomplished by restricting CPU usage between double thresholds and keeping the total utilization of the CPU by all the VMs allocated to a single server between these two thresholds. In addition, cloud module could migrate to other servers to either reduce the number of active servers or achieve better performance. In this case, the communication cost would be factored into the energy cost model. The performance of our algorithm is compared to algorithms such as the Pegasus Workflow Management system, Minimum Power Consumption Minimum Power Consumption (MPC-MPC) algorithm, and Greedy algorithm. The simulation results show that the Time and Energy Aware Scheduling heuristic can significantly decrease the power consumption of cloud servers with high resource utilization for the underlying clouds.